We do not rely on a supervised text extractor to pick out specific sub-spans of a sentence that represent a cause-effect pairing between propositions.We found poor annotator agreement on span boundaries in an initial investigation on crowdsourcing data for such a system; we intend to return to this in future work, investigating improvements to our results via trained extraction models for corpus pre-processing. We instead curate a series of patterns from previous studies [1]}, [2]}, [3]}. These patterns can be classified into two categories, according to how they are mostly used in language to convey a causal relation: 1. EPC (effect-pattern-cause) category: I am very sad because I lost my phone; 2. CPE (cause-pattern-effect) category: The earthquake resulted in many deaths. For EPC patterns, we simply take the text on the left of the pattern as effect, and take the text on the right of the pattern as cause. The case is reversed for CPE category patterns. These patterns (shown in Table REF ) were applied to the Common Crawl corpus, followed by post-filtering: duplicate removal; filtering explicitly negated relations and verbs in passive voice; and restricting the cause and effect to each contain at least two tokens. This results in our CausalBank corpus, denoted here as \(\mathcal {B}\) , with 133 M EPC + 181 M CPE = 314 M \((c,e)\)  (\(c\)  refers to cause and \(e\)  refers to effect) pairs in total. We manually evaluated 1,000 randomly sampled sentences from the corpus and found that 95% conveyed a meaningful causal relation.
